christinebuckler

-- This example decodes percent-encoded sequences from url parameters, 
-- replaces plus signs with spaces, and converts to all lowercase.

CREATE OR REPLACE FUNCTION schema_name.function_name(original TEXT)
    RETURNS VARCHAR IMMUTABLE AS
        $$
        import urllib
        u = urllib.unquote_plus(original)
        return u.lower()
        $$

rmdort

class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

erichurst

ZIP,LAT,LNG
00601,18.180555, -66.749961
00602,18.361945, -67.175597
00603,18.455183, -67.119887
00606,18.158345, -66.932911
00610,18.295366, -67.125135
00612,18.402253, -66.711397
00616,18.420412, -66.671979
00617,18.445147, -66.559696
00622,17.991245, -67.153993

yong27

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, kwargs = args
    return df.apply(func, **kwargs)

def apply_by_multiprocessing(df, func, **kwargs):
    workers = kwargs.pop('workers')